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WORKSHOP FOCUS
Intelligent
Tutoring Systems have made great strides in recent years. Robust ITSs
have been developed and deployed in arenas ranging from mathematics and
physics to engineering and chemistry. Over the past decade intelligent
tutoring systems have become increasingly accepted as viable teaching
and learning tools in academia and industry.
Most of the ITS research and development to this point has been done in
well-defined domains. Well-defined domains are characterized by a basic
formal theory or clear-cut domain model. Such domains are typically
quantitative, and are often taught by human tutors using problems where
answers can unambiguously be classified as correct or incorrect.
Well-defined domains are particularly amenable to model-tracing
tutoring systems. Operationalizing the domain theory makes it possible
to identify study problems, provide a clear problem solving strategy,
and assess results definitively based on the existence of unambiguous
answers. Help can be readily provided by comparing the students’
problem-solving steps to the existing domain models.
Not all domains of teaching and inquiry are well-defined, indeed most
are not. Domains such as law, argumentation, history, art, medicine,
and design are ill-defined. Often even well-defined domains are
increasingly ill-defined at the edges where new knowledge is being
discovered. Ill-defined domains lack well-defined models and formal
theories that can be operationalized, typically problems do not have
clear and unambiguous solutions. For this reason ill-defined domains
are typically taught by human tutors using exploratory, collaborative,
or Socratic instruction techniques.
Ill-defined domains present a number of unique challenges for
researchers in Intelligent Tutoring Systems and Computer Modeling.
These challenges include 1) Defining a viable computational model for
aspects of underspecified or open-ended domains; 2) Development of
feasible strategies for search and inference in such domains; 3)
Provision of feedback when the problem-solving model is not definitive;
4) Structuring of learning experiences in the absence of a clear
problem, strategy, and answer; 5) User models that accommodate the
uncertainty of ill-defined domains; and 6) User interface design for
ITSs in ill-defined domains where usually the learner needs to be
creative in his actions, but the system still has to be able to analyze
them.
These challenges must be faced if the ITS community is ever to branch
out from the traditional domains into newer arenas. Over the past few
years a number of researchers have begun work in ill-defined domains
including law, medicine, professional ethics and design. This
workshop represents a chance to share what has been learned by those
practitioners at a time when work in these domains is still
nascent.
CALL FOR PAPERS
We invite work at all stages of development, including
particularly innovative approaches in their early phases. Research
papers (up to 9 pages) and demonstrations (up to 4 pages, describing an
application or other work to be demonstrated live at the workshop) are
welcome for submission. Workshop topics include but are not limited to:
- Model
Development: Production of formal or informal models of ill-defined
domains or subsets of such domains.
- Teaching
Strategies: Development of teaching strategies for such domains, for
example, Socratic, problem-based, task-based, or exploratory strategies.
- Search
and Inference Strategies: Identification of exploration and inference
strategies for ill-defined domains such as heuristic searches and
case-based comparisons.
- Assessment:
Development of Student and Tutor assessment strategies for ill-defined
domains. These may include, for example, studies of related-problem
transfer and qualitative assessments.
- Feedback:
Identification of feedback and guidance strategies for ill-defined
domains. These may include, for example, Socratic (question-based)
methods or related-problem transfer.
- Exploratory
Systems: Development of intelligent tutoring systems for open-ended
domains. These may include, for example, user-driven “exploration
models” and constructivist approaches.
- Collaboration:
The use of peer-collaboration within ill-defined domains, e.g., to
ameliorate modeling issues.
- Representation:
Free form text is often the most appropriate representation for
problems and answers in ill-defined domains; intelligent tutoring
systems need techniques for accommodating that.
The
topics can be approached from different perspectives: theoretical,
systems engineering, application oriented, case study, system
evaluation, etc.
WORKSHOP
PROGRAM COMMITTEE
Vincent
Aleven, Carnegie Mellon University, USA
Jerry Andriessen, University of Utrecht, The Netherlands
Kevin Ashley, University of Pittsburgh, USA
Michael Baker, Centre National de la Recherche Scientifique, France
Paul Brna, University of Glasgow, UK
Robin Burke, DePaul University, USA
Jill Burstein, Educational Testing Service, USA
Rebecca Crowley, University of Pittsburgh, USA
Susanne Lajoie, McGill University, Canada
Collin Lynch, University of Pittsburgh, USA
Liz Masterman, Oxford University, UK
Bruce McLaren, Carnegie
Mellon University, USA
Antoinette
Muntjewerff, University of Amsterdam, The Netherlands
Katsumi Nitta, Tokyo Institute of Technology, Japan
Niels Pinkwart, Carnegie Mellon University, USA
Beverly Woolf, University of Massachusetts, USA
WORKSHOP
SCHEDULE
8.30 -
8.40 Introduction
8.40 - 9.45 Paper Session 1
Collin Lynch, Kevin Ashley, Vincent Aleven, and
Niels Pinkwart
(Carnegie Mellon University and University of Pittsburgh): Defining
Ill-Defined Domains; A literature survey
Nguyen-Thinh Le (University of Hamburg): A
Constraint-based Assessment
Approach for Free-Form Design of Class Diagrams using UML
Michael
Heilman and Maxine Eskenazi (Carnegie Mellon University):
Language Learning: Challenges for Intelligent Tutoring Systems
9.45 - 10.00 Coffee Break
10.00 - 11.30 Paper Session 2
Amy Ogan, Ruth Wylie, and Erin Walker (Carnegie
Mellon University): The
challenges in adapting traditional techniques for modeling student
behavior in ill-defined domains
Nguyen-Thinh Le (University of Hamburg): Using
Prolog Design Patterns
to Support Constraint-Based Error Diagnosis in Logic Programming
Vincent
Aleven, Niels Pinkwart, Kevin Ashley, and Collin Lynch (Carnegie Mellon
University and University of Pittsburgh): Supporting Self-explanation
of Argument Transcripts: Specific v. Generic Prompts
Amali Weerasinghe and Antonija Mitrovic
(University of Canterbury):
Individualizing Self-Explanation Support for Ill-Defined Tasks in
Constraint-based Tutors
11.30 - 12.00 General Discussion
12.00 - 13.30 Lunch Break
13.30 - 15.00 Paper Session 3
Toby Dragon and Beverly Park Woolf (University
of
Massachusetts-Amherst): Guidance and Collaboration Strategies in
Ill-defined Domains
Hao-Chuan Wang, Carolyn P. Rosé, Tsai-Yen
Li, and Chun-Yen Chang
(Carnegie Mellon University, National Chengchi University Taiwan and
National Taiwan Normal University): Providing Support for Creative
Group Brainstorming: Taxonomy and Technologies
Ilya
Goldin, Kevin Ashley, and Rosa Pinkus (University of Pittsburgh):
Teaching Case Analysis through Framing: Prospects for an ITS in an
ill-defined domain
Amy
Ogan, Vincent Aleven, and Christopher Jones (Carnegie Mellon
University): Culture in the Classroom: Challenges for Assessment in
Ill-Defined Domains
15.00 - 15.30 General Discussion
IMPORTANT
DATES
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April 7, 2006: Submission deadline
for workshop papers
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April 25, 2006: Acceptance notification
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May 10, 2006: Final version deadline for workshop papers
- June
27, 2006: Workshop
SUBMISSION GUIDELINES
Authors are
asked to use the ITS workshop paper template which can be downloaded
from here (MS Word) or here (LaTex).
Please submit your papers by email
to its06-workshop@lists.andrew.cmu.edu.
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